The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed inventions.
The technology disclosed relates to identifying entity reflections that refer to a same real-world entity. In particular, it relates to using statistical functions to make probabilistic deductions about entity attributes, which are used to construct optimal combinations of entity attributes. These optimal combinations of entity attributes are further used to generate search queries that return more precise search results with greater recall.
In this era of large electronic environments, where each individual is a social profile or business-to-business contact, there is an ever-increasing need of personalized tools that can gather credible information about individuals. For instance, news articles containing myriad information about individuals, do not provide users the tools to identify whether certain new articles belong to the individual in question or to another individual with the same name.
Accordingly, it is desirable to provide systems and methods that offer a flexible approach to identifying entity mentions that refer to a same real-world entity. An opportunity arises to provides users personalized tools that will allow them to identify whether or not a web mention or database profile belongs to a particular individual. Enhanced user experience and increased user satisfaction may result.